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All Journal Majalah Ilmiah UNIKOM Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science JTIM : Jurnal Teknologi Informasi dan Multimedia JIKA (Jurnal Informatika) J-Intech (Journal of Information and Technology) Jurnal Minfo Polgan (JMP) Indonesian Journal of Digital Business Jurnal Algoritma Jurnal Informatika dan Teknologi Pendidikan Journal of Artificial Intelligence and Digital Business Journal of Management and Innovation Entrepreneurship (JMIE) Jurnal Pengabdian Nasional (JPN) Indonesia
Guntara, Rangga Gelar
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Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Journal : Building of Informatics, Technology and Science

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Kombinasi Inisialisasi Hyperparameter dan Algoritma Adam untuk Optimasi Model Gated Recurrent Unit dalam Meramalkan Harga Penutupan Saham Pasca Isu Boikot Labibah, Bintu; Guntara, Rangga Gelar; Maesaroh, Syti Sarah
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6202

Abstract

This research aims to enhance the accuracy of daily closing price forecasting for PT Unilever Indonesia Tbk shares by optimizing a Gated Recurrent Unit (GRU) model, a deep learning architecture. The study focuses on the impact of boycott issues on company performance and explores the effectiveness of hyperparameter tuning and the Adam optimization algorithm. Utilizing a five-year historical dataset, time series analysis was employed. The results demonstrate that the GRU model with a configuration of 50 epochs, batch size 8, hidden state 64, and default Adam parameters achieved the lowest Mean Absolute Percentage Error (MAPE) of 1.59% and Root Mean Squared Error (RMSE) of 71.51 among the 81 configurations tested. While higher Adam parameter settings also yielded satisfactory results, this specific configuration exhibited superior performance. The findings highlight the sensitivity of model accuracy to sharp price fluctuations, suggesting that shorter forecast horizons may be more appropriate. This research contributes significantly to the advancement of accurate and reliable stock price forecasting models.
Co-Authors Adam Hermawan Adi Prehanto Amri, Teuku Khairul Ar-Ridlo, M Dzikri Ar-Ridlo, Muhammad Dzikri Asep Nuryadin Azzahra, Monica Salwa Fernando, Daud Fiqriansyah, Yogi Ilham Fitriani, Vivi Indah Gunawan Gunawan Herdiana, Oding Husniyah, Nadiyah Jaelani, Rusani Kashira, Frans Budi Labibah, Bintu Maesaroh, Syti Sarah Prameswari, Zahra Kirana Purwaamijaya, Btari Mariska Ramadansyah, Erif Ramadhan, Hafizh Arkaan Ramadhani, Raja Almas Setiawan, Amelia Ananda Siahaan, Michael Abraham Susanto, Fazry Rachman Ukasyah, Muhammad Utami, Mutiara Puteri Yullia Febrianti, Sheilla